Buffering is the source of our collective internet frustration when it comes to streaming content online with no real solution in sight. This might change, however, as a group of researchers from MIT CSAIL have created a neural network AI that may overcome the issue. Pensieve, as the system is called, uses a neural network to train itself on a range of network conditions and picks the best bit rate algorithms for the network condition.

Streaming websites such as YouTube take advantage of the Adaptive BitRate algorithms (ABR) to determine the quality at which the video will play. ABR either measures the speed of transmission of data that a network can undertake, or, maintain a buffer on the head of the video to get a head start, so to speak.

As time passes, the AI measures its performance and tunes the algorithm for best results. Comparing to Carnegie Mellon's "model predictive control" (MPC) method which makes optimizations based on its predictions of network change, Pensieve was able to achieve the same resolution with 10 to 30 percent less buffering.

MIT professor Mohammad Alizadeh, one of the three researchers spearheading the project, said in a statement:

"Our system is flexible for whatever you want to optimize it for. You could even imagine a user personalizing their own streaming experience based on whether they want to prioritize rebuffering versus resolution."

While the AI is primarily for services such as Netflix and YouTube, the professor added that he's excited to see what this technology might lend to the world beyond the screen- virtual reality. You can read the research paper here.